Automated airborne particulate matter collection, imaging, identification, and analysis

ABSTRACT

The following is an apparatus and a method that enables the automated collection and identification of airborne particulate matter comprising dust, pollen grains, mold spores, bacterial cells, and soot from a gaseous medium comprising the ambient air. Once ambient air is inducted into the apparatus, aerosol particulates are acquired and imaged under a novel lighting environment that is used to highlight diagnostic features of the acquired airborne particulate matter. Identity determinations of acquired airborne particulate matter are made based on captured images. Abundance quantifications can be made using identity classifications. Raw and summary information are communicated across a data network for review or further analysis by a user. Other than routine maintenance or subsequent analyses, the basic operations of the apparatus may use, but do not require the active participation of a human operator.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. Section 119(e) ofthe following co-pending and commonly-assigned U.S. provisional patentapplication(s), which is/are incorporated by reference herein:

Provisional Application Ser. No. 62/076,507, Filed on Nov. 7, 2014entitled “Continuous Automated Air Sampling Device That CommunicatesAcquired Images to a Network”.

TECHNICAL FIELD

The technical field to which the subject matter of this disclosurerelates is Environmental Technology.

BACKGROUND ART

(Note: This application references a number of different non-patentpublications as indicated throughout the specification by one or morereference numbers within braces, e.g., {x}. A list of these differentpublications ordered according to these reference numbers can be foundbelow in the section titled “References”. Each of these publications isincorporated by reference herein.)

The concentrations of aerosol particulate matter in the ambient is a topconcern to humankind because airborne particulates have been stronglytied to human health consequences by numerous epidemiological studies.Airborne particulates aggravate respiratory illness which is the singlelargest cause of hospital admissions among children in the United States{1} and is responsible for a cost upwards of $56 billion in terms ofhealth care expenses, lost productivity, and decreased quality of lifein the United States {2}. Short-term exposures (hours to several days)to elevated airborne particulate matter have been observed to exacerbateallergies and asthma {3-5}, Longer term exposures (years to decades) toelevated airborne particulate matter have substantially greater healthrisks such as increasing the probability of heart disease, diabetes, andother chronic disease {6, 7}, Given that the allergenic virulence ofsome airborne particulates has increased over the past three decades 81,the prevalence of allergies and asthma in the developed world hasgreatly increased over the same period {4-6, 9}, and that the expressionof asthma and allergies is forecasted to continue to intensify {10-12},it is important to develop effective mitigation strategies that swilltemper both the economic and health burdens caused by airborneparticulate-triggered respiratory illness. Knowing the types ofparticulates, their concentrations, and their distribution within alocal environment helps in diagnosis, avoidance, and effectivetreatment.

Additionally, airborne particulate matter is of horticultural,ecological, and biological interest as it has applications in thepropagation and health of plants as well as the expansion of scientificknowledge.

Air-quality sampling devices exist, but the ability of such devices todiscern characteristics of airborne particulate matter beyond size rangeand reflectivity is limited. Such devices are useful for determining thequantity of certain sizes of airborne particulate matter, but givelittle insight into the shape, color, or other physical or biologicalproperties of the airborne particulate matter, and thus are notpractical for discerning detail or identifying airborne particulatematter.

Given the differing effects various components of airborne particulatematter on human health and plant well-being, it is important to be ableto quickly and reliably characterize the constitution of airborneparticulate matter. What is needed is a system and method that automatesthe collection of the air sample and captures diagnostic images whichcan then be used to characterize the identity of airborne particulatematter.

The disclosure described herein collects, images, releases, analyzes,and identifies airborne particulate matter suspended in a gaseousmedium. The subject matter of the disclosure function with or withouthuman intervention, can discriminate between different types of airborneparticulate matter, and is more efficient and consistent than currentmethods due to the implementation of a novel collection apparatus andanalysis method.

BRIEF DESCRIPTION OF DRAWINGS

(Note: this disclosure references components within each figuredescribed below. The naming convention used throughout the specificationis first to list the figure number, followed by a decimal point,followed by the specific component number of the given figure, prefacedby the word “Figure” or “Figures”, as the situation demands, and allincluded within parentheses, e.g., (x). For example, a reference to theillustration of the induction unit would be made as follows: (FIG.1.2).)

The features and advantages of the disclosure will become clearer withthe following detailed description in connection with the accompanyingdrawings, wherein:

FIG. 1 is a front-view illustration depicting an embodiment of a portionof the disclosure, with the following components identified:

1: ambient air

2: induction unit

3: airborne particulate inlet aperture

4: weather-resistant enclosure de-emphasized and indicated by a dashedline

5: air chamber

6: electrode (may be the anode)

7: an embodiment of airborne particulate matter

8: deposition surface (or medium of deposition)

9: translation or rotation mechanism

10: spacer tube—part of the perception unit

11: high-resolution magnified digital camera—part of the perception unit

12: pixel light ring—part of the perception unit

13: objective lens—part of the perception unit

14: linear focus apparatus part of the perception unit

15: main controller board and on-board computer with integrated Wi-ficommunication capability

16: motor controllers

17: high-voltage electric field generator unit

18: filter

19: power and network cabling

20: sampling disk and with embedded electrode (may be the cathode). Thiscomponent bears the deposition surface (FIGS. 1.8 and 3.8) and is themedium of deposition

24: environmental sensors

26: screw stepper.

FIG. 2 is an illustration of a front oblique view some features havebeen omitted for clarity) depicting an embodiment of the disclosure,with the following components identified:

2: induction unit

3: airborne particulate inlet aperture

5: static charge air chamber and electrode

10: spacer tube

12: pixel light ring with reflective light baffles

13: objective lens

20: sampling disk and with embedded electrode (may be the cathode). Thiscomponent bears the deposition surface (FIGS. 1.8 and 3.8)

21: cleaning mechanism electrode.

FIG. 3 is an illustration of a back oblique view (some features havebeen omitted for clarity) depicting an embodiment of the disclosure,with the following components identified:

2: induction unit

3: airborne particulate inlet aperture

5: air chamber

6: electrode (may be the anode)

8: deposition surface

10: spacer tube

12: light pixel ring with reflective light baffles

13: objective lens

18: filter

20: sampling disk and with embedded electrodes

22: deposition surface cleaning area

23: cleaning brush

25: imaging area.

FIG. 4 is an illustration of a rear oblique view (some features havebeen omitted for clarity) depicting an embodiment of the disclosure,with the following components identified:

2: induction unit

3: airborne particulate inlet aperture

9: translation or rotation mechanism

10: spacer tube

12: light pixel ring with reflective baffles

13: objective lens

14: focus mechanism comprising a linear rail, a motor and end-stops

18: fiber

20: sampling disk and with embedded electrode (may be the cathode). Thiscomponent bears the deposition surface (FIGS. 1.8 and 3.8)

25: imaging area

26: screw stepper.

FIG. 5 is an illustration of a front view depicting an embodiment of thedisclosure, detailing the flow of particulates through the system withthe following components identified:

1: an airborne particulate (enlarged and not to scale) enters theairborne particulate inlet aperture

2: electrostatic charge imparted to particulate(s)

3: particulate(s) deposited on deposition surface

4: illumination and imaging of particulate(s)

5: brush, airstream, electrostatic charge, gravity, and filter cleandeposition surface.

FIG. 6 A flowchart representing the steps of the analysis method forcollecting, observing, and identifying airborne particulate matterdispersed in a gaseous medium with the following components identified:

1: collect airborne particulate matter onto the surface of a depositionmedium

2: use an imaging device directed towards the medium of deposition toassess the locations and sizes of acquired particulates within the fieldof view, may be accomplished through image segmentation

3: determine the optimal focus location for each particulate using afocus assessment function limited to each particulate's segment boundary

4: capture one or more images from many different lightingconfigurations for each particle at the ideal local location

5: store captured images in a local or remote data repository foridentity determination or other analysis.

BEST MODE FOR CARRYING OUT THE INVENTION

The subject matter of this disclosure represents an automated,computerized, electro-mechanical apparatus (FIG. 1, FIG. 2, FIG. 3, FIG.4, and FIG. 5) utilizing a combination of hardware and software toacquire, image, identify, quantify, and communicate resulting datadescribing airborne particulate matter (FIG. 1.7, FIG. 6) dispersed in agaseous medium (FIG. 1.1). Other than installation, the continued supplyof the requisite electrical power (FIG. 1.19), the continuity of acommunication network (FIG. 1.19 and FIG. 1.15), and periodicmaintenance to the apparatus, the operations of the disclosure describedherein can have, but do not require, the presence or actions of a humanoperator. The typical composition of airborne particulate matter inambient air is comprised of a complex mixture of dust, pollen grains,fungal spores, bacterial cells, viruses, by-products of internal enginecombustion that comprise components of air pollution includinghydrocarbons, and other compounds. The resulting data, which are the netproduct of the apparatus and its associated software, are images,identities, and quantifications of aerosol particulates. Such data maybe used for a variety of purposes which include improving the diagnosisand avoidance of airborne particulate matter-related human healthissues, plant reproduction, and other air pollution applications.

The disclosure is comprised of the following components: a collectionsystem (FIG. 1.2, FIG. 1.3, FIG. 1.5, FIG. 1.6, FIG. 1.8, and FIG.1.20); a lighting and imaging system (FIG. 1.10, FIG. 1.11, FIG. 1.12,FIG. 1.13, and FIG. 1.14); a release and cleaning system (FIG. 1.2, FIG.1.18, FIG. 2.21, FIG. 3.18, FIG. 3.22, and FIG. 3.23); and an analysismethod (FIG. 6). Each component is described in greater detail below.

The collection system enables the acquisition of airborne particulatematter from the ambient air. An embodiment of this disclosure mayutilize an induction unit comprising, but not limited to, a blower fan(FIGS. 1,2, 2.2, 3.2 and 4.2) or other air-flow mechanism to drawambient air (FIG. 1.1) from the atmosphere into the device through anaperture (FIGS. 1.3, 2.3, 3.3 and 4.3) that may project through anenclosure FIG. 1.4). The size of the aperture is not important exceptthat it must be known for determining the volume of air flow forcalibration and airborne particulate matter quantification purposes.After passing through the aperture (FIGS. 1.3, 2,3, 3.3 and 4.3) andentering the device, the inducted air stream may enter into a largerventuri air chamber (FIGS. 1,5, 2.5, 3.5), and may pass a smallelectrode (FIGS. 1.6 and 3.6). The electrode may be negatively charged(an anode) and may act to increase the negative charge of passingairborne particulates (FIG. 1.7). A high-voltage electric fieldgenerator unit (FIG. 1.17) may create an electrostatic force and mayalso act to generate an oppositely charged field on a second electrode(a cathode) which may be situated under the deposition surface (FIG. 1.8and FIG. 3.8). The electrostatic force created between the negativelycharged airborne particulate matter and the positive electric fieldinduced on the deposition surface may draw airborne particulates ontothe deposition surface (FIG. 1.8 and FIG. 3.8), also referred to as themedium of deposition (FIG. 1.20), Periodically, a motor (FIGS. 1.9 and4.9) or other translation or rotation mechanism (FIGS. 1.20, 2.20, 3.20and 4.20) may engage to move the deposition surface such that additionalairborne particulate matter may deposit on a different portion of thedeposition surface (FIG. 1.8, FIG. 3.8, and FIG. 1.20). An embodiment ofthe disclosure may utilize a rotating disk with an embedded electrode(FIGS. 2.20, 3.20 and 4.20) that is oppositely charged relative to theairborne particulate matter and which may attract the airborneparticulates to the edge of a disk (FIGS. 1.8, 3.8 and 5.3). Thedeposition surface is continuous, allowing the observation of airborneparticulate matter to occur simultaneous with collection or after adelay following collection.

In an embodiment of the disclosure, the collection system maytemporarily shut off and discontinue acquisition of airborne particulatematter from the ambient air in the event of inclement weatherconditions. Inclement weather may comprise stormy conditions withabnormally high levels of wind that may allow moisture or excessivelevels of dust to enter the aperture (FIG. 1.3), information regardingthe local weather or other environmental information used to determinecontrol of the collection system may be retrieved over a communicationnetwork via a cable (FIG. 1.19), a Wi-Fi connection integrated with theonboard computer (FIG. 1.15), or from environmental sensors (FIG. 1.24)comprising air pressure, humidity, and temperature, integrated with themain controller board and on-board computer (FIG. 1.15).

The lighting and imaging system enables the capture, recording, andstorage of images of sufficient quality for analyses and identificationof the acquired airborne particulate matter. In an embodiment of thedisclosure, a motor (FIGS. 1.9 and 4.9) may rotate the depositionsurface (FIG. 1.8, FIG. 3.8, and FIG. 1.20) and thus convey the acquiredairborne particulate matter from the collection area to the imaging area(FIG. 3.25 and FIG. 4.25). The imaging area (FIG. 3.25 and FIG. 4.25)may be encased in glass to avoid air flow at the imaging location. Anembodiment of this disclosure may utilize a finite objective lens (FIG.1.13), a spacing tube (FIG. 1.10), and a high-resolution magnifiedcamera (FIG. 1.11) or other optical or electronic sensor technology andan associated light/radiation system (FIG. 1.12, FIG. 2.12, and FIG.3.12) to capture and record digital representations of the acquiredairborne particulate matter. The objective lens (FIG. 1.13), spacingtube (FIG. 1.10), a high-resolution magnified camera (FIG. 1.11) orother electronic/optical sensor technology, and associatedlight/radiation system (FIG. 1.12, FIG. 2.12, and FIG. 3.12) comprisethe perception unit for human or machine observation of the airborneparticulate matter. Magnification of the perception unit may beaccomplished via optical means, digital means or both and may comprisean objective lens or series of lenses (FIG. 1.13), a spacing tube (FIG.1.10), a high-resolution magnified camera (FIG. 1.11), or other opticalor electronic sensor technology. The field of view of the objective lens(FIG. 1.13, FIG. 2.13, FIG. 3.13 and FIG. 4.13) may be recorded with adigital camera (FIG. 1.11 and FIG. 4.11) or other electronic/opticalsensor technology that may be oriented so as to examine the depositionsurface (FIG. 1.8, FIG. 3.8, and FIG. 1.20) and the acquired airborneparticulate matter thereon.

For lighting, an embodiment of this disclosure may use multiple lightand electromagnetic radiation sources, individually controlled, andsituated in a ring (FIG. 1.12) surrounding the acquired airborneparticulate matter on the deposition surface (FIG. 1,8, FIG. 3.8, andFIG. 1.20). The lighting apparatus may utilize red-green-blue (RGB)light emitting diodes (LED) or other electromagnetic radiation sourcessimilarly situated within the pixel light ring (FIG. 1.12) to illuminatethe deposition surface (FIG. 1.8, FIG. 3.8, and FIG. 1.20) within thefield of view of the objective lens (FIG. 2.13, FIG. 3.13, and FIG.4.13) with ultraviolet, visible, and/or near infrared electromagneticlight at a high angle of incidence onto the top adaxial surface of theairborne particulate matter. An embodiment of the disclosure may alsoinclude reflective light baffles on the pixel light ring (FIG. 3.12 andFIG. 4.12) to increase the amount of diffuse light in addition to directlight incident to the acquired airborne particulate matter on thedeposition surface (FIG. 1.8, FIG. 3.8, and FIG. 1.20). A control unitcomprised of a software system, which may be integrated with the maincontroller board (FIG. 1.15), may individually modulate and rotate theLED pixels through a variety of wavelength combinations from 200 to 800nanometers and lighting perspectives while the software may cause imagesof the acquired airborne particulate matter to be captured under eachlighting combination in order to obtain a series of obliquely lit,dark-field images captured from different adaxial and diffuseillumination directions, over a range of the electromagneticfrequencies, and across a range of illumination intensities. Obliquelylit and dark-field refer to the adaxial orientation of pixel light ring(FIG. 1.12) and the high-resolution magnified digital camera (FIG. 1.11)relative to the acquired airborne particulate matter on the depositionsurface (FIG. 1.8, FIG. 3.8, and FIG. 1.20) which is illuminated withlight and other electromagnetic radiation both on its top surface and/ordiffusely. To reduce camera noise, multiple camera frames may becaptured of each perspective and lighting combination, and the framesmay be averaged together into a composite image. Knowledge of thedirection, frequency, and intensity of the light source may be used tohighlight and infer topological features of the acquired airborneparticulate matter including, pores and furrows of pollen grains andseptae or mycelia fragments of spores.

In an embodiment of this disclosure, the imaging system may have alinear focus apparatus (FIG. 1.14) which may allow the software to seekout ideal focus on a particulate-specific basis. The high-resolutionmagnified digital camera (FIG. 1.11), the spacer tube (FIG. 1.10), theobjective lens (FIG. 1.13), or the pixel light ring (FIG. 1.12) may rideon a linear carriage (FIGS. 1.14 and 4.14) to facilitate focusing, winchcarriage may be translated via a screw stepper (FIG. 1.26 and FIG. 4))controlled by a motor controller (FIG. 1.16). Focus may be accomplishedin two phases: coarse and fine. Using the digital camera (FIG. 1.11),the computer control system may assess the best focus for the overallfield of view (the coarse focus) by incrementally advancing the focusmotor (FIG. 4.14) and processing the image through a focus assessmentfunction. Such focus function may consist of bilinear squared differencefactored with the overall histogram width. Once the optimal focuslocation is determined for the overall field of view, image segmentationalgorithms (described below) may be used to determine individualparticulate locations and sizes. A fine focus process may then beperformed whereby the focal point may be translated through a rangeproximal to the overall optimal. Through this process, the software maytake multiple image samples constrained to the boundaries of eachsegment and at ranged focal positions. The optimal focus position foreach segment region may then be determined by taking the best of thefocus values for that region. This focus position may then be used asthe focus position in the subsequent imaging of each region.

The release and cleaning system enables the evacuation and discharge ofthe acquired airborne particulate matter front the deposition surface(FIG. 1.8, FIG. 3.8, and FIG. 1.20). Once a portion of the depositionsurface (FIG. 1.8, FIG. 3.8, and FIG. 1.20) has been imaged, it may becleaned before being rotated or translated back into the collection areanear the airborne particulate inlet aperture (FIG. 1.3, FIG. 2.3, FIG.3.3, and FIG. 4.3). Release and cleaning of airborne particulate mattermay be accomplished via one or more of the following mechanisms or acombination thereof: reversing the electric charge; airflow; mechanical;physical; gravity; or filter. In an embodiment of the disclosure, thedeposition surface (FIG. 1,8, FIG. 3.8, and FIG. 1.20) may be rotated orotherwise translated into a region (FIG. 3.22) where oppositely chargedelectrodes (FIG. 2.21), a foam or brush cleaning mechanism (FIG. 3.23),passing air being evacuated from the device which may be from the actionof the induction unit (FIGS. 1.2. 2.2, 3.2 and 4.2), and gravity maycombine to remove the acquired airborne particulate matter and carry itor let it fall passively into a filter (FIGS. 1.18, 3.18, 4.18 and 5.5).

In an embodiment of the disclosure, an electric field may be utilized torepel the acquired airborne particulate matter front the depositionsurface (FIG. 1.8, FIG. 3.8, and FIG. 1.20). The creation of an electricfield of opposite polarity to that used for collection, between anelectrode beneath the deposition surface (FIG. 1.8, FIG. 3.8, and FIG.1.20) and another electrode (FIG. 2.21), which may be a plate or ringbeneath an air filter (FIGS. 1.18 and 3.18), may result in a strongrepulsive force being exerted on to the acquired airborne particulatematter located on the deposition surface (FIG. 1.8, FIG. 3.8, and FIG.1.20). Air flow may additionally be concentrated on the cleaning area(FIG. 3.22) generating an additional force due to a relative differenceof atmospheric pressure on the acquired airborne particulate matterlocated on the deposition surface (FIG. 1.8, FIG. 3.8, and FIG. 1.20)and creating an additional cleaning effect. Furthermore,, a charged oruncharged piece of foam, brush, sponge, stopper, or other physicalobject (FIG. 3.23) may be used to generate a physical force on theacquired airborne particulate matter located on the deposition surface(FIG. 1.8, FIG. 3.8, and FIG. 1.20) and mechanically remove persistentacquired airborne particulate matter located on the deposition surface(FIG. 1.8, FIG. 3.8, and FIG. 1.20). In an embodiment of the disclosure,the deposition surface cleaning area (FIG. 3.22) may be oriented in sucha way that a gravitational force acts on the acquired airborneparticulate matter located on the deposition surface (FIG. 1.8, FIG.3.8, and FIG. 1.20), encouraging the acquired airborne particulatematter to drop away from the deposition surface (FIG. 1.8, FIG. 3.8, andFIG. 1.20) within the deposition surface cleaning area (FIG. 3.22) andmay be in combination with the exertion of an electrostatic force, aforce created from the relative difference of atmospheric pressure, andphysical force. in an embodiment of this disclosure, the electrostatic,difference in atmospheric pressure, physical, and gravitational forcesrepresent separate removal mechanisms and may all be presentindividually or in combination within a concentrated area (FIG. 3.22) togive maximal cleaning and avoid contaminating of the deposition surface(FIG. 1.8, FIG. 3.8, and FIG. 1.20) during sampling collection cycles.

The analysis method of this disclosure (FIG. 6) enables thedetermination of the identity of acquired airborne particulate matter.The images acquired by an embodiment of this disclosure may be processedpartially or completely by the computer processor within the disclosureor may be transmitted to off-site servers for partial or completeprocessing. Image processing may comprise three dimensional (3D)reconstruction from lighting, image compositing, correction, or otherprocessing to enhance image quality including, but not limited to,histogram equalization, sharpness enhancement, and edge detection.Images may be analyzed manually by a human, via automated countingalgorithms, or both. After analysis, the images may be kept in a storagerepository for later analysis, or may be discarded. In an embodiment ofthis disclosure, the device may send raw imagery to a cloud servicewirelessly or over a network cable (FIG. 1.19), but may also run aneural network-based algorithm (described below) trained to recognizevarious identities of acquired airborne particulate matter includingspecies, genera, or family of pollen grains; species, genera, or familyof fungal spores; species, genera, or family of bacterial cells; speciesof internal engine combustion by-products; or the identity of otherairborne particulate matter.

An embodiment of this disclosure may identify acquired airborneparticulate matter in the captured digital images by using imagesegmentation algorithms, known informally as “blobbing” techniques.Maximally Stable Extremal Regions (MSER) {13} and other algorithmsproduce a series of regions pertaining to each identified feature. Eachidentified acquired airborne particulate matter feature may be known asa segment. For segments larger than a configured threshold, thesegment's image may be passed into a proximal classifier function whichmay determine whether the segment is most likely to be a) a singlesimple object; b) a single compound object; or c) a cluster of objects.If the segment appears to be a cluster, a declustering functionutilizing concavity and seam based splitting may iteratively breakoverlapping segments into a series of masked out sub-segments. Theclassifier function thus described may be necessary to identify certainairborne particulate matter such as pollen which may have compoundfeatures, resulting in concavities.

For each segment (acquired airborne particulate matter featuresidentified by the MSER or similar algorithm), or group of segments ifthere are multiple acquired airborne particulate matter features locatedat the same optimal focus location, an embodiment's software may step tosaid focus position and engage the lighting and imaging system describedabove. The lighting and capture system involves sequentially turning onLED lights (FIGS. 1.12, 2.12, 3.12 and 4.12) and capturing imageslighted under various illumination intensities and from variousillumination perspectives. RGB LED lights may be strategically placed ona reflective surface (FIGS. 1.12, 2.12, 3.12 and 4.12) around theperimeter of the objective lens (FIG. 1.13 and FIG. 2.13) and may beindividually controlled to illuminate the acquired airborne particulatematter from a multitude of perspectives, light intensity levels, andwavelengths. Acquired airborne particulate matter may be sequentiallyimaged with oblique lighting from multiple vantage points, highlightingthe features, topology, transparency, and absorption at lightwavelengths from 200 to 800 nanometers.

In an embodiment of the disclosure, multiple obliquely lit imagesegments may be used to infer three dimensional (3D) shapecharacteristics and surface features of the acquired airborneparticulate matter. Light transmissibility and reflectivity may also beinferred using this multiple lighting angle approach. To infer 3Dfeatures, a software model may be constructed starting with a malleableprimitive object, a sphere may be used as a reasonable malleableprimitive object. The model may be scaled to match the maximal extent ofthe aggregate captured image and may then be sculpted inward based onthe perimeter shape. Highlights from each directionally lit image may bethen used to push or pull portions of the model according to the 3Dvector of the particular light. If the object's facing surface isdetermined to be convex, highlights that appear on the side opposite thelight may be treated as being on the far end of the translucent object,thus shaping may be possible on both the facing and opposing sides. Oncea 3D representation of the object is constructed, its position may benormalized, a color or texture is applied to it based on the capturedimage(s) of the acquired airborne particulate matter, and may berendered with high-contrast lighting. The resulting rendering may becomposed with the original image, or may be used for direct observation.Alternatively, the 3D representation may serve as input to a classifierthat is suitable for working with 3D models.

An embodiment of this disclosure may implement machine vision torecognize and classify acquired airborne particulate matter via a neuralnetwork classifier. Prior to classification, various imagepre-processing commonly used in machine vision may be applied,comprising histogram equalization, sharpness enhancement, and edgedetection. Neural networks may be generally defined by a set ofinterconnected input “neurons”. The connections may have numeric weightsthat can be tuned based on experience, making the neural networkadaptive to inputs and thus capable of learning. The characterizationalgorithms may activate and weight neurons by the pixel values of anindividual input image. After initial weighting, the values may bepassed to other neurons where they may be transformed b other functionsrelative to a library of identification criteria and then may be passedon again to other neurons. This process may be iterative and may repeatuntil the output neuron is activated and classification probabilitiesare achieved. The resulting probabilities may be further weighed andidentification may be achieved using a statistical model of spatial andtemporal abundance of the airborne particulate matter, so that aparticular acquired airborne particulate matter such as a pollen grainthat may be deemed by the classifier to be equally likely to be eitherof two genera, will be weighted towards the genera that is most likelyfor that location and time of year,

Segment images and associated analysis data may be, in an embodiment ofthis disclosure, available online to those authorized to access them. Inaddition to displaying the images, a human operator may be able to givefeedback regarding classification, which feedback and corrections may beused to improve the training of the classifier. The series ofsegmentation images, comprising the digital imagery or particle topologyinputs as well as initial particle determinations, may be used directlyor with further processing as inputs to classification software or maybe analyzed by a human, either remotely or locally, to produce airborneparticulate matter identity determinations. The statistical, spatial andtemporal model may also be improved, using data coming from devices, aswell as correctional feedback from software users.

In an embodiment of the disclosure, captured images may be processed bythe onboard computer (FIG. 1.15), and the resulting images and data maybe stored until internet communication is established with a remotenetwork storage system via a cable (FIG. 1.19) or Wi-Fi connectionintegrated with the onboard computer (FIG. 1.15). The computer processorwithin the disclosure (FIG. 1.15) or off-site cloud servers may make thecaptured images and count results available online and via pushnotification. Quantification of the acquired airborne particulate matterbased on the time each portion of the deposition surface (FIG. 1.8, FIG.3.8, and FIG. 1.20) was actively sampled and collected may beaccomplished and included with the results.

The disclosure has been described in an illustrative manner, and it isto be understood that the terminology which has been used is intended tobe in the nature of words of description rather than of limitation.Obviously, many modifications and variations of the present disclosureare possible in light of the above teachings. For example, the devicecould include mechanisms to direct air currents strategically or toprotect itself from adverse environmental and weather conditions.Embodiments may use various deposition media on which to collectparticulates, various methods for positioning the particulates forimaging, and various methods of lighting and imaging specimens.Embodiments may also implement practical variants by allowing usersflexibility in the kinds of enclosures and mounting mechanisms.

REFERENCES

1. Akinbami, L., Asthma prevalence, health care use and mortality,2003-05. Guide for State Health Agencies in the Development of AsthmaPrograms; US Department of Health and Human Services, Centers forDisease Control and Prevention. CDC. Available at:www.cdc.gov/nchs/products/pubs/pubd/hestats/asthma/asthma.htm. 2006.2. Barnett, S. B. L. and T. A. Nurmagambetov, Costs of asthma in theUnited States: 2002-2007. Journal of Allergy and Clinical Immunoloey,2011. 127(1): p. 145-152.3. Lucas, R. W., J. Dees, R. Reynolds, B. Rhodes, and R. W. Hendershot,Cloud-computing and smartphones: Tools for improving asthma managementand understanding environmental triggers. Annals of Allergy, Asthma &Immunology, 2015. 114: p. 431-432.4. Eder, W., M. J. Ege, and E. von Mutius, Current concepts: The asthmaepidemic. New England Journal of Medicine, 2006. 355(21): p. 2226-2235.5. Beggs, P. J., C. H. Katelaris, D. Medek, F. H. Johnston, P. K.Burton, B. Campbell, A. K. Jaggard, D, Vicendese, Bowman, I. Godwin, A.R. Huete, B. Erbas, B. J. Green, R. M. Newnham, E. Newbigin, S. G.Haberle, and J. M. Davies, Differences in grass pollen allergen exposureacross Australia. Australian and New Zealand Journal of Public Health,2015. 39(1); p. 51-55.6. Anderson, H. R., G. Favarato, and R. W. Atkinson, Long exposure tooutdoor air pollution and the prevalence of asthma: meta:-analysis ofmulti-community prevalence studies. Air Quality Atmosphere and Health,2013. 6(1): p. 57-68.7. Brook, R. D., S. Cakmak, M. C. Turner, J. R. Brook, D. L. Crouse, P.A. Peters. A. van Donkelaar, P. J. Villeneuve, O. Brion, M. Jerrett, R.V. Martin, S. Rajagopalan, M. S. Goldberg, C. A. Pope, and R. T.Burnett, Long-Term Fine Particulate Matter Exposure and Mortality FromDiabetes in Canada. Diabetes Care, 2013, 36(10): p. 3313-3320.8. Zhang, Y., L. Bielory, Z. Y. Mi, T. Cai, A. Robock, and P.Georgopoulos, Allergenic pollen season variations in the past twodecades under changing climate in the United States. Global ChangeBiology, 2015. 21(4): p. 1581-1589.9. Moorman, J., L. Akinbami, C. Bailey, and et al., NationalSurveillance of Asthma: United States, 2001-2010. National Center forHealth Statistics. Vital Health Stat., 2012. 3(35).10. Zhang, Y., L. Bielory, T. Cai., Z. Mi, and P. Georgopoulos,Predicting onset and duration of airborne allergenic pollen season inthe United States. Atmospheric Environment, 2015. 103: p. 297-306.11. Eggen, B., S. Vardoulakis, D. Hemming, and Y. Clewlow. Pollenforecasting, climate change & public health. in int. Conf On ClimateChange Effects. 2013.12. Myszkowska, D. and R. Majewska, Pollen grains as allergenicenvironmental factors—new approach to the forecasting of the pollenconcentration during the season. Annals of agricultural andenvironmental medicine: AAEM, 2014. 21(4): p. 68-688.

13. Wassenberg, J., Bulatov, D., Middelmann, W., Sanders, P.:Determination of Maximally Stable Extremal Regions in Large Images. In:Signal Processing, Pattern Recognition, and Applications. (February2008).

1. An apparatus to collect and observe airborne particulate matterdispersed in a gaseous medium comprised of: a. a deposition surface ontowhich the airborne particulate matter will collect; b. an electric fieldgenerator unit for attracting and holding the airborne particulatematter to the deposition surface; and c. a perception unit for human ormachine observation of the airborne particulate matter on the depositionsurface.
 2. The apparatus according to claim I, wherein the depositionsurface is continuous, allowing the observation to occur simultaneouswith the collection or after a delay following the collection.
 3. Theapparatus according to claim 1, wherein a release and cleaning system isemployed to remove the airborne particulate matter from the depositionsurface after observation; which is comprised of electrical,atmospheric, physical, and gravitational force components actingindividually or in combination.
 4. The apparatus according to claim 1,wherein an induction unit is used to draw and/or evacuate ambient airinto and out of the apparatus.
 5. The apparatus according to claim 1,wherein an air flow control unit is used to regulate the rate of airinduction into the apparatus relative to an ambient environment externalto the apparatus.
 6. The apparatus according to claim 1, wherein aperception unit includes magnification components either optical ordigital or both for enhanced observation of the airborne particulatematter.
 7. The apparatus according to claim 1, wherein a lighting andimaging system is employed to capture, record, store, and/or communicatedigital representations of the airborne particulate matter.
 8. Theapparatus according to claim 1, wherein the collected airborneparticulate matter comprise but are not limited to pollen grains, fungalspores, soot, dust, bacterial cells, and internal engine combustionby-products that are components in air pollution from a portion ofambient air drawn into the apparatus.
 9. The apparatus according toclaim 1, whereby environmental sensors are integrated with theapparatus.
 10. The apparatus according to claim 9, whereby saidenvironmental sensors or weather data retrieved over a communicationnetwork are used to determine control of the said apparatus in the eventof inclement weather conditions, including the temporary shutting off ofthe apparatus.
 11. The apparatus according to claim 1, wherein theapparatus can have, but does not require, the presence or actions of ahuman operator except for installation, the continued supply of therequisite electrical power, the continuity of the communication network,and periodic maintenance to the apparatus.
 12. An apparatus toilluminate and capture images of airborne particulate matter collectedon a deposition surface comprised of: a. multiple controlled lightsources situated in a ring surrounding the airborne particulate matterand emitting ultraviolet, visible, and/or near infrared electromagneticlight at a high angle of incidence to the top surface of the airborneparticulate matter; b. an imaging unit oriented to observe the airborneparticulate matter and situated adaxial to the light sources; c. acontrol unit for the light sources and the imaging unit whichindividually modulates the light sources and activates the imaging unitso as to capture a series of images of the airborne particulate matterilluminated from different adaxial directions, over a range of theelectromagnetic frequencies, and across a range of illuminationintensities.
 13. The apparatus according to claim 12, whereby knowledgeof the direction, frequency, and intensity of the light source is usedby software to infer the topology of the airborne particulate matter.14. The apparatus according to claim 12, whereby the series of imagesare used directly or with further processing as inputs to classificationsoftware or are analyzed by a human to produce airborne particulatematter identity determinations.
 15. The system according to claim 14,whereby particle identification is achieved by weighing initial saidparticle type determinations against a statistical model derived from adatabase of spatial and temporal abundance of the airborne particulatematter.
 16. A particle identification system which operates on digitalimagery of airborne particulate matter topology data, comprising: a. asegmentation function to isolate the particle locations within the inputdigital imagery or particle topology; b. a proximal classificationfunction to determine if each segment of the input digital imagery orparticle topology represents a single simple object, a cluster ofobjects, or a single compound object; c. a declustering function that,if a said segment is determined to be a cluster, will iterativelydecluster said segment into a series of sub-segments; and d. a finalclassification function which determines the mostly likely particle typefor each said segment
 16. The system according to claim 16, whereby ahuman operator is presented with a software user interface, remotely orlocally, comprising the digital imagery or particle topology inputs aswell as initial particle determinations and is given the opportunity tocorrect the particle determinations of each said segment, whichcorrection is used for analysis or to improve said classificationsystem.
 18. (canceled)